code: def bottleneck_block(self, input, num_filters, stride, name):
      shifted = self.shift_module(input)
      conv0 = self.conv_bn_layer(
          input=shifted,
          num_filters=num_filters,
          filter_size=1,
          act="relu",
          name=name + "_branch2a",
      )
      conv1 = self.conv_bn_layer(
          input=conv0,
          num_filters=num_filters,
          filter_size=3,
          stride=stride,
          act="relu",
          name=name + "_branch2b",
      )
      conv2 = self.conv_bn_layer(
          input=conv1,
          num_filters=num_filters * 4,
          filter_size=1,
          act=None,
          name=name + "_branch2c",
      )
  
      short = self.shortcut(
          input, num_filters * 4, stride, name=name + "_branch1"
      )
  
      add = paddle.add(x=short, y=conv2)
      return paddle.nn.functional.relu(add)
  
file_path: DALI/docs/examples/use_cases/paddle/tsm/tsm.py
imports: import math
  import paddle
  from paddle import static
label_desc: Use image node_id 5 for calling the TSM obj's underlying member method code with example usage: obj.bottleneck_block(input, num_filters, stride, name) and returns: paddle